dblp: Jake M. Hofman
https://dblp.org/pid/63/5865.html
dblp person page RSS feedThu, 26 Dec 2024 00:50:32 +0100en-USdaily1released under the CC0 1.0 licensedblp@dagstuhl.de (dblp team)dblp@dagstuhl.de (dblp team)Computers/Computer_Science/Publications/Bibliographieshttp://www.rssboard.org/rss-specificationhttps://dblp.org/img/logo.144x51.pngdblp: Jake M. Hofmanhttps://dblp.org/pid/63/5865.html14451Using Open Data to Automatically Generate Localized Analogies.https://doi.org/10.1145/3613904.3642638Sofia Eleni Spatharioti, Daniel G. Goldstein, Jake M. Hofman: Using Open Data to Automatically Generate Localized Analogies.CHI2024: 1036:1-1036:13]]>https://dblp.org/rec/conf/chi/SpathariotiGH24Mon, 01 Jan 2024 00:00:00 +0100Multi-_target Multiplicity: Flexibility and Fairness in _target Specification under Resource Constraints.https://doi.org/10.1145/3593013.3593998Jamelle Watson-Daniels, Solon Barocas, Jake M. Hofman, Alexandra Chouldechova: Multi-_target Multiplicity: Flexibility and Fairness in _target Specification under Resource Constraints.FAccT2023: 297-311]]>https://dblp.org/rec/conf/fat/Watson-DanielsB23Sun, 01 Jan 2023 00:00:00 +0100Multi-_target Multiplicity: Flexibility and Fairness in _target Specification under Resource Constraints.https://doi.org/10.48550/arXiv.2306.13738Jamelle Watson-Daniels, Solon Barocas, Jake M. Hofman, Alexandra Chouldechova: Multi-_target Multiplicity: Flexibility and Fairness in _target Specification under Resource Constraints.CoRRabs/2306.13738 (2023)]]>https://dblp.org/rec/journals/corr/abs-2306-13738Sun, 01 Jan 2023 00:00:00 +0100Comparing Traditional and LLM-based Search for Consumer Choice: A Randomized Experiment.https://doi.org/10.48550/arXiv.2307.03744Sofia Eleni Spatharioti, David M. Rothschild, Daniel G. Goldstein, Jake M. Hofman: Comparing Traditional and LLM-based Search for Consumer Choice: A Randomized Experiment.CoRRabs/2307.03744 (2023)]]>https://dblp.org/rec/journals/corr/abs-2307-03744Sun, 01 Jan 2023 00:00:00 +0100Comparing scalable strategies for generating numerical perspectives.https://doi.org/10.48550/arXiv.2308.01535Hancheng Cao, Sofia Eleni Spatharioti, Daniel G. Goldstein, Jake M. Hofman: Comparing scalable strategies for generating numerical perspectives.CoRRabs/2308.01535 (2023)]]>https://dblp.org/rec/journals/corr/abs-2308-01535Sun, 01 Jan 2023 00:00:00 +0100REFORMS: Reporting Standards for Machine Learning Based Science.https://doi.org/10.48550/arXiv.2308.07832Sayash Kapoor, Emily Cantrell, Kenny Peng, Thanh Hien Pham, Christopher A. Bail, Odd Erik Gundersen, Jake M. Hofman, Jessica Hullman, Michael A. Lones, Momin M. Malik, Priyanka Nanayakkara, Russell A. Poldrack, Inioluwa Deborah Raji, Michael Roberts, Matthew J. Salganik, Marta Serra-Garcia, Brandon M. Stewart, Gilles Vandewiele, Arvind Narayanan: REFORMS: Reporting Standards for Machine Learning Based Science.CoRRabs/2308.07832 (2023)]]>https://dblp.org/rec/journals/corr/abs-2308-07832Sun, 01 Jan 2023 00:00:00 +0100Pre-registration for Predictive Modeling.https://doi.org/10.48550/arXiv.2311.18807Jake M. Hofman, Angelos Chatzimparmpas, Amit Sharma, Duncan J. Watts, Jessica Hullman: Pre-registration for Predictive Modeling.CoRRabs/2311.18807 (2023)]]>https://dblp.org/rec/journals/corr/abs-2311-18807Sun, 01 Jan 2023 00:00:00 +0100How Good is Good Enough? Quantifying the Impact of Benefits, Accuracy, and Privacy on Willingness to Adopt COVID-19 Decision Aids.https://doi.org/10.1145/3488307Gabriel Kaptchuk, Daniel G. Goldstein, Eszter Hargittai, Jake M. Hofman, Elissa M. Redmiles: How Good is Good Enough? Quantifying the Impact of Benefits, Accuracy, and Privacy on Willingness to Adopt COVID-19 Decision Aids.DTRAP3(3): 27:1-27:18 (2022)]]>https://dblp.org/rec/journals/dtrap/KaptchukGHHR22Sat, 01 Jan 2022 00:00:00 +0100Investigating Perceptual Biases in Icon Arrays.https://doi.org/10.1145/3491102.3501874Cindy Xiong, Ali Sarvghad, Daniel G. Goldstein, Jake M. Hofman, Çagatay Demiralp: Investigating Perceptual Biases in Icon Arrays.CHI2022: 137:1-137:12]]>https://dblp.org/rec/conf/chi/XiongSGHD22Sat, 01 Jan 2022 00:00:00 +0100Round Numbers Can Sharpen Cognition.https://doi.org/10.1145/3491102.3501852Huy Anh Nguyen, Jake M. Hofman, Daniel G. Goldstein: Round Numbers Can Sharpen Cognition.CHI2022: 375:1-375:15]]>https://dblp.org/rec/conf/chi/NguyenHG22Sat, 01 Jan 2022 00:00:00 +0100Putting scientific results in perspective: Improving the communication of standardized effect sizes.https://doi.org/10.1145/3491102.3502053Yea-Seul Kim, Jake M. Hofman, Daniel G. Goldstein: Putting scientific results in perspective: Improving the communication of standardized effect sizes.CHI2022: 625:1-625:14]]>https://dblp.org/rec/conf/chi/KimHG22Sat, 01 Jan 2022 00:00:00 +0100Manipulating and Measuring Model Interpretability.https://doi.org/10.1145/3411764.3445315Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake M. Hofman, Jennifer Wortman Vaughan, Hanna M. Wallach: Manipulating and Measuring Model Interpretability.CHI2021: 237:1-237:52]]>https://dblp.org/rec/conf/chi/Poursabzi-Sangdeh21Fri, 01 Jan 2021 00:00:00 +0100Datamations: Animated Explanations of Data Analysis Pipelines.https://doi.org/10.1145/3411764.3445063Xiaoying Pu, Sean Kross, Jake M. Hofman, Daniel G. Goldstein: Datamations: Animated Explanations of Data Analysis Pipelines.CHI2021: 467:1-467:14]]>https://dblp.org/rec/conf/chi/PuKHG21Fri, 01 Jan 2021 00:00:00 +0100How Visualizing Inferential Uncertainty Can Mislead Readers About Treatment Effects in Scientific Results.https://doi.org/10.1145/3313831.3376454Jake M. Hofman, Daniel G. Goldstein, Jessica Hullman: How Visualizing Inferential Uncertainty Can Mislead Readers About Treatment Effects in Scientific Results.CHI2020: 1-12]]>https://dblp.org/rec/conf/chi/HofmanGH20Wed, 01 Jan 2020 00:00:00 +0100Expanding the Scope of Reproducibility Research Through Data Analysis Replications.https://doi.org/10.1145/3366424.3383417Jake M. Hofman, Daniel G. Goldstein, Siddhartha Sen, Forough Poursabzi-Sangdeh: Expanding the Scope of Reproducibility Research Through Data Analysis Replications.WWW (Companion Volume)2020: 567-571]]>https://dblp.org/rec/conf/www/HofmanG0P20Wed, 01 Jan 2020 00:00:00 +0100How good is good enough for COVID19 apps? The influence of benefits, accuracy, and privacy on willingness to adopt.https://arxiv.org/abs/2005.04343Gabriel Kaptchuk, Daniel G. Goldstein, Eszter Hargittai, Jake M. Hofman, Elissa M. Redmiles: How good is good enough for COVID19 apps? The influence of benefits, accuracy, and privacy on willingness to adopt.CoRRabs/2005.04343 (2020)]]>https://dblp.org/rec/journals/corr/abs-2005-04343Wed, 01 Jan 2020 00:00:00 +0100To Put That in Perspective: Generating Analogies that Make Numbers Easier to Understand.https://doi.org/10.1145/3173574.3174122Christopher Riederer, Jake M. Hofman, Daniel G. Goldstein: To Put That in Perspective: Generating Analogies that Make Numbers Easier to Understand.CHI2018: 548]]>https://dblp.org/rec/conf/chi/RiedererHG18Mon, 01 Jan 2018 00:00:00 +0100Manipulating and Measuring Model Interpretability.http://arxiv.org/abs/1802.07810Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake M. Hofman, Jennifer Wortman Vaughan, Hanna M. Wallach: Manipulating and Measuring Model Interpretability.CoRRabs/1802.07810 (2018)]]>https://dblp.org/rec/journals/corr/abs-1802-07810Mon, 01 Jan 2018 00:00:00 +0100What's Happening and What Happened: Searching the Social Web.https://doi.org/10.1145/3091478.3091484Omar Alonso, Vasileios Kandylas, Serge-Eric Tremblay, Jake M. Hofman, Siddhartha Sen: What's Happening and What Happened: Searching the Social Web.WebSci2017: 191-200]]>https://dblp.org/rec/conf/websci/AlonsoKTHS17Sun, 01 Jan 2017 00:00:00 +0100The Structural Virality of Online Diffusion.https://doi.org/10.1287/mnsc.2015.2158Sharad Goel, Ashton Anderson, Jake M. Hofman, Duncan J. Watts: The Structural Virality of Online Diffusion.Manag. Sci.62(1): 180-196 (2016)]]>https://dblp.org/rec/journals/mansci/GoelAHW16Fri, 01 Jan 2016 00:00:00 +0100Improving Comprehension of Numbers in the News.https://doi.org/10.1145/2858036.2858510Pablo Javier Barrio, Daniel G. Goldstein, Jake M. Hofman: Improving Comprehension of Numbers in the News.CHI2016: 2729-2739]]>https://dblp.org/rec/conf/chi/BarrioGH16Fri, 01 Jan 2016 00:00:00 +0100Exploring Limits to Prediction in Complex Social Systems.https://doi.org/10.1145/2872427.2883001Travis Martin, Jake M. Hofman, Amit Sharma, Ashton Anderson, Duncan J. Watts: Exploring Limits to Prediction in Complex Social Systems.WWW2016: 683-694]]>https://dblp.org/rec/conf/www/MartinHSAW16Fri, 01 Jan 2016 00:00:00 +0100Exploring limits to prediction in complex social systems.http://arxiv.org/abs/1602.01013Travis Martin, Jake M. Hofman, Amit Sharma, Ashton Anderson, Duncan J. Watts: Exploring limits to prediction in complex social systems.CoRRabs/1602.01013 (2016)]]>https://dblp.org/rec/journals/corr/MartinHSAW16Fri, 01 Jan 2016 00:00:00 +0100Split-door criterion for causal identification: Automatic search for natural experiments.http://arxiv.org/abs/1611.09414Amit Sharma, Jake M. Hofman, Duncan J. Watts: Split-door criterion for causal identification: Automatic search for natural experiments.CoRRabs/1611.09414 (2016)]]>https://dblp.org/rec/journals/corr/SharmaHW16Fri, 01 Jan 2016 00:00:00 +0100Estimating the Causal Impact of Recommendation Systems from Observational Data.https://doi.org/10.1145/2764468.2764488Amit Sharma, Jake M. Hofman, Duncan J. Watts: Estimating the Causal Impact of Recommendation Systems from Observational Data.EC2015: 453-470]]>https://dblp.org/rec/conf/sigecom/SharmaHW15Thu, 01 Jan 2015 00:00:00 +0100Scalable Recommendation with Hierarchical Poisson Factorization.http://auai.org/uai2015/proceedings/papers/208.pdfPrem Gopalan, Jake M. Hofman, David M. Blei: Scalable Recommendation with Hierarchical Poisson Factorization.UAI2015: 326-335]]>https://dblp.org/rec/conf/uai/GopalanHB15Thu, 01 Jan 2015 00:00:00 +0100Estimating the Causal Impact of Recommendation Systems from Observational Data.http://arxiv.org/abs/1510.05569Amit Sharma, Jake M. Hofman, Duncan J. Watts: Estimating the Causal Impact of Recommendation Systems from Observational Data.CoRRabs/1510.05569 (2015)]]>https://dblp.org/rec/journals/corr/SharmaHW15Thu, 01 Jan 2015 00:00:00 +0100A large-scale exploration of group viewing patterns.https://doi.org/10.1145/2602299.2602309Allison June-Barlow Chaney, Mike Gartrell, Jake M. Hofman, John Guiver, Noam Koenigstein, Pushmeet Kohli, Ulrich Paquet: A large-scale exploration of group viewing patterns.TVX2014: 31-38]]>https://dblp.org/rec/conf/tvx/ChaneyGHGKKP14Wed, 01 Jan 2014 00:00:00 +0100Sharding social networks.https://doi.org/10.1145/2433396.2433424Quang Duong, Sharad Goel, Jake M. Hofman, Sergei Vassilvitskii: Sharding social networks.WSDM2013: 223-232]]>https://dblp.org/rec/conf/wsdm/DuongGHV13Tue, 01 Jan 2013 00:00:00 +0100Scalable Recommendation with Poisson Factorization.http://arxiv.org/abs/1311.1704Prem Gopalan, Jake M. Hofman, David M. Blei: Scalable Recommendation with Poisson Factorization.CoRRabs/1311.1704 (2013)]]>https://dblp.org/rec/journals/corr/GopalanHB13Tue, 01 Jan 2013 00:00:00 +0100Who Does What on the Web: A Large-Scale Study of Browsing Behavior.http://www.aaai.org/ocs/index.php/ICWSM/ICWSM12/paper/view/4660Sharad Goel, Jake M. Hofman, M. Irmak Sirer: Who Does What on the Web: A Large-Scale Study of Browsing Behavior.ICWSM2012]]>https://dblp.org/rec/conf/icwsm/GoelHS12Sun, 01 Jan 2012 00:00:00 +0100Everyone's an influencer: quantifying influence on twitter.https://doi.org/10.1145/1935826.1935845Eytan Bakshy, Jake M. Hofman, Winter A. Mason, Duncan J. Watts: Everyone's an influencer: quantifying influence on twitter.WSDM2011: 65-74]]>https://dblp.org/rec/conf/wsdm/BakshyHMW11Sat, 01 Jan 2011 00:00:00 +0100Who says what to whom on twitter.https://doi.org/10.1145/1963405.1963504Shaomei Wu, Jake M. Hofman, Winter A. Mason, Duncan J. Watts: Who says what to whom on twitter.WWW2011: 705-714]]>https://dblp.org/rec/conf/www/WuHMW11Sat, 01 Jan 2011 00:00:00 +0100Graphical models for inferring single molecule dynamics.https://doi.org/10.1186/1471-2105-11-S8-S2Jonathan E. Bronson, Jake M. Hofman, Jingyi Fei, Ruben L. Gonzalez, Chris H. Wiggins: Graphical models for inferring single molecule dynamics.BMC Bioinform.11(S-8): S2 (2010)]]>https://dblp.org/rec/journals/bmcbi/BronsonHFGW10Fri, 01 Jan 2010 00:00:00 +0100Predicting consumer behavior with Web search.https://doi.org/10.1073/pnas.1005962107Sharad Goel, Jake M. Hofman, Sébastien Lahaie, David M. Pennock, Duncan J. Watts: Predicting consumer behavior with Web search.Proc. Natl. Acad. Sci. USA107(41): 17486-17490 (2010)]]>https://dblp.org/rec/journals/pnas/GoelHLPW10Fri, 01 Jan 2010 00:00:00 +0100Inferring relevant social networks from interpersonal communication.https://doi.org/10.1145/1772690.1772722Munmun De Choudhury, Winter A. Mason, Jake M. Hofman, Duncan J. Watts: Inferring relevant social networks from interpersonal communication.WWW2010: 301-310]]>https://dblp.org/rec/conf/www/ChoudhuryMHW10Fri, 01 Jan 2010 00:00:00 +0100Characterizing individual communication patterns.https://doi.org/10.1145/1557019.1557088R. Dean Malmgren, Jake M. Hofman, Luís A. Nunes Amaral, Duncan J. Watts: Characterizing individual communication patterns.KDD2009: 607-616]]>https://dblp.org/rec/conf/kdd/MalmgrenHAW09Thu, 01 Jan 2009 00:00:00 +0100Characterizing Individual Communication Patterns.http://arxiv.org/abs/0905.0106R. Dean Malmgren, Jake M. Hofman, Luís A. Nunes Amaral, Duncan J. Watts: Characterizing Individual Communication Patterns.CoRRabs/0905.0106 (2009)]]>https://dblp.org/rec/journals/corr/abs-0905-0106Thu, 01 Jan 2009 00:00:00 +0100